Stanford University SEO = search engine optimization Approximately 10‐15% of web pages are spam...

CS246: Mining Massive DatasetsJure Leskovec, Stanford University

http://cs246.stanford.edu

Rank nodes using link structure

PageRank: Link voting: P with importance x has n out‐links, each link gets x/n votes Page R’s importance is the sum of the votes on its in‐links

Complications: Spider traps, Dead‐ends At each step, random surfer has two options: With probability , follow a link at random With prob. 1‐, jump to some page uniformly at random

2/12/2012 Jure Leskovec, Stanford C246: Mining Massive Datasets 2

Measures generic popularity of a page Biased against topic‐specific authorities Solution: Topic‐Specific PageRank (next)

Uses a single measure of importance Other models e.g., hubs‐and‐authorities Solution: Hubs‐and‐Authorities (next)

Susceptible to Link spam Artificial link topographies created in order to boost page rank Solution: TrustRank (next)


Instead of generic popularity, can we measure popularity within a topic?

Goal: Evaluate Web pages not just according to their popularity, but by how close they are to a particular topic, e.g. “sports” or “history.”

Allows search queries to be answered based on interests of the user Example: Query “Trojan” wants different pages depending on whether you are interested in sports or history.

2/12/2012 4Jure Leskovec, Stanford C246: Mining Massive Datasets

Assume each walker has a small probability of “teleporting” at any step

Teleport can go to: Any page with equal probability To avoid dead‐end and spider‐trap problems

A topic‐specific set of “relevant” pages (teleport set) For topic‐sensitive PageRank.

Idea: Bias the random walk When walked teleports, she pick a page from a set S S contains only pages that are relevant to the topic E.g., Open Directory (DMOZ) pages for a given topic

For each teleport set S, we get a different vector rS2/12/2012 5Jure Leskovec, Stanford C246: Mining Massive Datasets

Let: Aij = Mij + (1‐) /|S| if iS

Mij otherwise A is stochastic!

We have weighted all pages in the teleport set S equally Could also assign different weights to pages!

Compute as for regular PageRank: Multiply by M, then add a vector Maintains sparseness


1

2 3

4

Suppose S = {1}, = 0.8

Node Iteration0 1 2… stable

1 1.0 0.2 0.52 0.2942 0 0.4 0.08 0.1183 0 0.4 0.08 0.3274 0 0 0.32 0.261

Note how we initialize the PageRank vector differently from the unbiased PageRank case.

0.2

0.50.5

1

1 1

0.4 0.4

0.8

0.8 0.8


Create different PageRanks for different topics The 16 DMOZ top‐level categories: arts, business, sports,…

Which topic ranking to use? User can pick from a menu Classify query into a topic Can use the context of the query E.g., query is launched from a web page talking about a known topic History of queries e.g., “basketball” followed by “Jordan”

User context, e.g., user’s bookmarks, …2/12/2012 8Jure Leskovec, Stanford C246: Mining Massive Datasets

Spamming: any deliberate action to boost a web page’s position in search engine results, incommensurate with page’s real value

Spam: web pages that are the result of spamming

This is a very broad definition SEO industry might disagree! SEO = search engine optimization

Approximately 10‐15% of web pages are spam


Early search engines: Crawl the Web Index pages by the words they contained Respond to search queries (lists of words) with the pages containing those words

Early Page Ranking: Attempt to order pages matching a search query by “importance” First search engines considered: 1) Number of times query words appeared. 2) Prominence of word position, e.g. title, header.


As people began to use search engines to find things on the Web, those with commercial interests tried to exploit search engines to bring people to their own site – whether they wanted to be there or not.

Example: Shirt‐seller might pretend to be about “movies.”

Techniques for achieving high relevance/importance for a web page


How do you make your page appear to be about movies? 1) Add the word movie 1000 times to your page Set text color to the background color, so only search engines would see it 2) Or, run the query “movie” on your target search engine See what page came first in the listings Copy it into your page, make it “invisible”

These and similar techniques are term spam


Believe what people say about you, rather than what you say about yourself Use words in the anchor text (words that appear underlined to represent the link) and its surrounding text

PageRank as a tool to measure the “importance” of Web pages


Our hypothetical shirt‐seller loses Saying he is about movies doesn’t help, because others don’t say he is about movies His page isn’t very important, so it won’t be ranked high for shirts or movies

Example: Shirt‐seller creates 1000 pages, each links to his with “movie” in the anchor text These pages have no links in, so they get little PageRank So the shirt‐seller can’t beat truly important moviepages like IMDB


Once Google became the dominant search engine, spammers began to work out ways to fool Google

Spam farms were developed to concentrate PageRank on a single page

Link spam: Creating link structures that boost PageRank of a particular page


Three kinds of web pages from a spammer’s point of view: Inaccessible pages Accessible pages: e.g., blog comments pages spammer can post links to his pages

Own pages: Completely controlled by spammer May span multiple domain names


Spammer’s goal: Maximize the PageRank of target page t

Technique: Get as many links from accessible pages as possible to target page t Construct “link farm” to get PageRank multiplier effect


Inaccessible

t

Accessible Own

1

2

M

One of the most common and effective organizations for a link farm


Millions of farm pages

x: PageRank contributed by accessible pages y: PageRank of target page t Rank of each “farm” page

where

Very small; ignore


N…# pages on the webM…# of pages spammer owns

Inaccessible

t

Accessible Own

12

M

where

For = 0.85, 1/(1‐2)= 3.6

Multiplier effect for “acquired” PageRank By making M large, we can make y as large as we want


N…# pages on the webM…# of pages spammer owns

Inaccessible

t

Accessible Own

12

M

Combating term spam Analyze text using statistical methods Similar to email spam filtering Also useful: Detecting approximate duplicate pages

Combating link spam Detection and blacklisting of structures that look like spam farms Leads to another war – hiding and detecting spam farms

TrustRank = topic‐specific PageRank with a teleport set of “trusted” pages Example: .edu domains, similar domains for non‐US schools


Basic principle: Approximate isolation It is rare for a “good” page to point to a “bad” (spam) page

Sample a set of “seed pages” from the web

Have an oracle (human) identify the good pages and the spam pages in the seed set Expensive task, so we must make seed set as small as possible


Call the subset of seed pages that are identified as “good” the “trusted pages”

Perform a topic‐sensitive PageRank with teleport set = trusted pages. Propagate trust through links: Each page gets a trust value between 0 and 1

Use a threshold value and mark all pages below the trust threshold as spam


Set trust of each trusted page to 1 Suppose trust of page p is tp Set of out‐links op

For each qop, p confers the trust: tp /|op| for 0 << 1

Trust is additive Trust of p is the sum of the trust conferred on p by all its in‐linked pages

Note similarity to Topic‐Specific PageRank Within a scaling factor, TrustRank = PageRank with trusted pages as teleport set


Trust attenuation: The degree of trust conferred by a trusted page decreases with distance

Trust splitting: The larger the number of out‐links from a page, the less scrutiny the page author gives each out‐link Trust is “split” across out‐links


Two conflicting considerations: Human has to inspect each seed page, so seed set must be as small as possible

Must ensure every “good page” gets adequate trust rank, so need make all good pages reachable from seed set by short paths


Suppose we want to pick a seed set of k pages

PageRank: Pick the top k pages by PageRank Theory is that you can’t get a bad page’s rank really high

Use domains whose membership is controlled, like .edu, .mil, .gov


In the TrustRank model, we start with good pages and propagate trust

Complementary view:What fraction of a page’s PageRank comes from “spam” pages?

In practice, we don’t know all the spam pages, so we need to estimate


r(p) = PageRank of page p

r+(p) = page rank of p with teleport into “good” pages only

Then:r‐(p) = r(p) – r+(p)

Spam mass of p = r‐(p)/ r (p)


SimRank: Random walks from a fixed node on k‐partite graphs

Setting: a k‐partite graph with k types of nodes Example: picture nodes and tag nodes.

Do a Random‐Walk with Restarts from a node u i.e., teleport set = {u}.

Resulting scores measures similarity to node u Problem: Must be done once for each node u Suitable for sub‐Web‐scale applications


34

ICDM

KDD

SDM

Philip S. Yu

IJCAI

NIPS

AAAI M. Jordan

Ning Zhong

R. Ramakrishnan

…

…

… …

Conference Author

Q:What is most relatedconference to ICDM ?

2/12/2012 Jure Leskovec, Stanford C246: Mining Massive Datasets

0.009

0.011

0.0080.007

0.005

0.005

0.0050.004

0.004

0.004


HITS (Hypertext‐Induced Topic Selection) is a measure of importance of pages or documents, similar to PageRank Proposed at around same time as PageRank (‘98)

Goal: Imagine we want to find good newspapers Don’t just find newspapers. Find “experts” – people who link in a coordinated way to good newspapers

Idea: Links as votes Page is more important if it has more links In‐coming links? Out‐going links?


Hubs and AuthoritiesEach page has 2 scores: Quality as an expert (hub): Total sum of votes of pages pointed to

Quality as an content (authority): Total sum of votes of experts

Principle of repeated improvement


NYT: 10

Ebay: 3

Yahoo: 3

CNN: 8

WSJ: 9

Interesting pages fall into two classes:1. Authorities are pages containing

useful information Newspaper home pages Course home pages Home pages of auto manufacturers

2. Hubs are pages that link to authorities List of newspapers Course bulletin List of US auto manufacturers


NYT: 10Ebay: 3Yahoo: 3CNN: 8WSJ: 9


Each page starts with hub score 1Authorities collect their votes

(Note this is idealized example. In reality graph is not bipartite and each page has both the hub and authority score)


Hubs collect authority scores



Authorities collect hub scores


A good hub links to many good authorities

A good authority is linked from many good hubs

Model using two scores for each node: Hub score and Authority score Represented as vectors h and a


Each page i has 2 scores: Authority score: Hub score:

HITS algorithm: Initialize: Then keep iterating: Authority: →

Hub: →

normalize: ,


[Kleinberg ‘98]

i

j1 j2 j3 j4

→

j1 j2 j3 j4

→

i

HITS converges to a single stable point Slightly change the notation: Vector a = (a1…,an), h = (h1…,hn) Adjacency matrix (n x n): Aij=1 if ij

Then:

So: And likewise:


j

jijiji

ji aAhah

aAh hAa T

45

[Kleinberg ‘98]

The hub score of page i is proportional to the sum of the authority scores of the pages it links to: h = λ A a Constant λ is a scale factor, λ=1/hi

The authority score of page i is proportional to the sum of the hub scores of the pages it is linked from: a = μ AT h Constant μ is scale factor, μ=1/ai


The HITS algorithm: Initialize h, a to all 1’s Repeat: h = A a Scale h so that its sums to 1.0 a = AT h Scale a so that its sums to 1.0

Until h, a converge (i.e., change very little)


1 1 1A = 1 0 1

0 1 0

1 1 0AT = 1 0 1

1 1 0

a(yahoo)a(amazon)a(m’soft)

===

111

111

14/51

10.751

. . .

. . .

. . .

10.7321

h(yahoo) = 1h(amazon) = 1h(m’soft) = 1

12/31/3

10.730.27

. . .

. . .

. . .

1.0000.7320.268

10.710.29


YahooYahoo

M’softM’softAmazonAmazon

HITS algorithm in new notation: Set: a = h = 1n

Repeat: h = A a, a = AT h Normalize

Then: a=AT(A a)

Thus, in 2k steps: a=(AT A)k ah=(A AT)k h


new hnew a

a is being updated (in 2 steps):AT(A a)=(AT A) a

h is updated (in 2 steps):A (AT h)=(A AT) h

Repeated matrix powering49

h = λ A a a = μ AT h h = λ μ A AT h a = λ μ AT A a

Under reasonable assumptions about A, the HITS iterative algorithm converges to vectors h* and a*: h* is the principal eigenvector of matrix A AT

a* is the principal eigenvector of matrix AT A


λ=1/hiμ=1/ai

PageRank and HITS are two solutions to the same problem: What is the value of an in‐link from u to v? In the PageRank model, the value of the link depends on the links into u In the HITS model, it depends on the value of the other links out of u

The destinies of PageRank and HITS post‐1998 were very different


Techniques for achieving high relevance/importance for a web page

1) Term spamming Manipulating the text of web pages in order to appear relevant to queries

2) Link spamming Creating link structures that boost PageRank or Hubs and Authorities scores


Repetition: of one or a few specific terms e.g., free, cheap, viagra Goal is to subvert TF‐IDF ranking schemes

Dumping: of a large number of unrelated terms e.g., copy entire dictionaries

Weaving: Copy legitimate pages and insert spam terms at random positions

Phrase Stitching: Glue together sentences and phrases from different sources


Analyze text using statistical methods e.g., Naïve Bayes classifiers Similar to email spam filtering

Also useful: detecting approximate duplicate pages


Stanford University SEO = search engine optimization Approximately 10‐15% of web pages are spam...

Documents

Transcript of Stanford University SEO = search engine optimization Approximately 10‐15% of web pages are spam...